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2014issue C0156-60

Constructing a MESA stochastic with roofing and SuperSmoother filters

This article reconstructs a SuperSmoother, a roofing filter, and a MESA stochastic oscillator from their stated lookbacks and coefficients.

  • The SuperSmoother is a two-pole recursion with a 10-bar cutoff, and the current input is the average of this close and the prior close.
  • The roofing stage applies a high-pass recursion whose alpha is computed from a 48-bar period, then feeds that output into the same 10-bar SuperSmoother.
  • The MESA stochastic takes a 20-bar highest-high and lowest-low ratio of the roofed series and then applies the SuperSmoother to that ratio.
  • Example systems treat 0.2 as the oversold threshold and 0.8 as the overbought threshold for a countertrend reverse.
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Current trackMaximum entropy spectrum analysis30 readingsTrend filter137 readingsStochastic oscillator170 readings

Three linked components

The 2014 implementations reconstruct three linked components from one article: a SuperSmoother, a roofing filter, and a MESA stochastic oscillator.

The SuperSmoother is a two-pole low-pass recursion whose coefficients are derived from a chosen cutoff so lag is smaller than that of a comparable moving average.

The roofing filter is the high-pass plus smoother cascade that removes spectral dilation before the stochastic ratio is computed.

The stochastic oscillator is a bounded oscillator that rescales a filtered price series between its recent high and low over a fixed lookback, then optionally smooths that ratio.

SuperSmoother lookback and input

The SuperSmoother is specified as a two-pole recursion whose coefficients use a cutoff of 10 bars, with the current input taken as the average of this close and the prior close.

Platform notes describe the SuperSmoother as a substitute for moving averages intended to reduce aliasing noise while keeping lag smaller than typical smoothing methods.

Roofing filter lookbacks

The roofing stage first applies a high-pass recursion whose alpha is computed from a 48-bar period, then feeds that output into the same 10-bar SuperSmoother.

That two-stage sequence is the trend filter in this construction: it first removes very slow drift and then attenuates high-frequency noise so the remaining series can feed an oscillator.

The roofing filter is presented as the stage that removes spectral dilation before the stochastic ratio is computed.

MESA stochastic ratio

The MESA stochastic is constructed by taking a 20-bar highest-high/lowest-low ratio of the roofed series and then applying the SuperSmoother to that ratio.

The coefficients follow a maximum entropy spectrum design so the filter passband is specified rather than inherited from a simple moving average.

Example oscillator extremes

Example systems treat 0.2 and 0.8 as oscillator extremes.

A long is taken when the MESA stochastic crosses below the oversold threshold at 0.2, and the position is reversed when it exceeds the overbought threshold at 0.8.

Portable reconstructions

Multiple charting platforms published parallel reconstructions of the same three indicators plus an example strategy, confirming the construction is portable across formula languages.

MESA Stochastic on daily Dollar General

Length-20 MESA Stochastic on daily Dollar General pins at the 1.0 cap through mid-July and repeatedly reaches the 0.2 floor that the tips treat as an entry extreme. Values were read from the eSignal subplot; the closing 0.31 is the labeled last print, not an interpolation.
Length-20 MESA Stochastic on daily Dollar General pins at the 1.0 cap through mid-July and repeatedly reaches the 0.2 floor that the tips treat as an entry extreme. Values were read from the eSignal subplot; the closing 0.31 is the labeled last print, not an interpolation.DG (Dollar General) · daily · 2013-04-01T00:00:00.000Z to 2013-10-18T00:00:00.000Z

Digitized from the eSignal daily pane titled MesaStochastic_Indicator DG D (20). Y-values are approximate to about two decimals except the labeled last print of 0.31. Horizontal guides at 0.8 and 0.2 are the overbought and oversold extremes named in the Traders’ Tips write-up. The length-20 lookback is the study parameter shown on the chart.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
27 of 28 in the Maximum entropy spectrum analysis track
201616-17 pp.Next on Maximum entropy spectrum analysisConstructing spectral heatmaps for dominant market cyclesA dominant-cycle estimate is a power-weighted average of candidate periods, and it is left unchanged when accumulated spectral power falls below 0.25.
All readings on this track · 28 readings
  1. 1984Constructing maximum-entropy spectra for dominant-cycle forecasts
  2. 1984How to construct a maximum-entropy cycle model
  3. 1984Constructing a maximum-entropy forecast from a chosen lookback
  4. 1985Constructing period-locked half-cycle and full-cycle averages
  5. 1986Why Fourier windows limit dominant-cycle resolution
  6. 1987Assembling short-lookback maximum-entropy cycle forecasts
  7. 1988Why a fitted dominant cycle is not a forecast
  8. 1989Evaluating commodity cycle personalities with spectral histograms
  9. 1989Evaluating next-session cycle forecasts with stops
  10. 1989Constructing cycle-aged volatility trailing stops
  11. 1990A channel signal-to-noise gate for dominant-cycle forecasts
  12. 1990Year-over-year dominant cycle personality audit
  13. 1991Cyclic entry from a locked dominant-cycle phase
  14. 1992Stationarity states on synchronized futures spectral contours
  15. 1997Hidden horizon assumptions in dominant-cycle readings
  16. 1997When market cycles are absent more than present
  17. 1997A spectral estimator that retunes indicators to the measured cycle
  18. 2000Constructing a Hilbert dominant cycle and a maximum-entropy refinement
  19. 2000Switch trend and cycle indicators after a half-cycle dwell test
  20. 2000Constructing a dominant-cycle squelch trend filter
  21. 2000Phasor displays for dominant-cycle construction
  22. 2002Low-lag trendline from elliptic and dominant-cycle notches
  23. 2004Spectral peaks are mode diagnostics, not forecasts
  24. 2004Compressive last-stage oscillator construction
  25. 2013Constructing trend failure curves from qualified-trend transitions
  26. 2014Lookback range, a two-lag smoother, and next-bar fills
  27. 2014Constructing a MESA stochastic with roofing and SuperSmoother filters
  28. 2016Constructing spectral heatmaps for dominant market cycles
By fieldIndicators and filters682 readingsBy year201489 readings
All 30 readings tagged Maximum entropy spectrum analysis
Also on Maximum entropy spectrum analysis5 readings
2000Switch trend and cycle indicators after a half-cycle dwell test2000Constructing a dominant-cycle squelch trend filter2002Low-lag trendline from elliptic and dominant-cycle notches2004Compressive last-stage oscillator construction2014Lookback range, a two-lag smoother, and next-bar fills